For optimal performance, you can probably just use an array of longs rather than a list.
We had a similar requirement at one point to implement a download time estimator, and we used a circular buffer to store the speed over each of the last N
seconds.
We weren't interested in how fast the download was over the entire time, just roughly how long it was expected to take based on recent activity but not so recent that the figures would be jumping all over the place (such as if we just used the last second to calculate it).
The reason we weren't interested in the entire time frame was that a download could so 1M/s for half an hour then switch up to 10M/s for the next ten minutes. That first half hour will drag down the average speed quite severely, despite the fact that you're now downloading quite fast.
We created a circular buffer with each cell holding the amount downloaded in a 1-second period. The circular buffer size was 300, allowing for 5 minutes of historical data, and every cell was initialised to zero. In your case, you would only need ten cells.
We also maintained a total (the sum of all entries in the buffer, so also initially zero) and the count (initially zero, obviously).
Every second, we would figure out how much data had been downloaded since the last second and then:
- subtract the current cell from the total.
- put the current figure into that cell and advance the cell pointer.
- add that current figure to the total.
- increase the count if it wasn't already 300.
- update the figure displayed to the user, based on total / count.
Basically, in pseudo-code:
def init (sz):
buffer = new int[sz]
for i = 0 to sz - 1:
buffer[i] = 0
total = 0
count = 0
index = 0
maxsz = sz
def update (kbps):
total = total - buffer[index] + kbps # Adjust sum based on deleted/inserted values.
buffer[index] = kbps # Insert new value.
index = (index + 1) % maxsz # Update pointer.
if count < maxsz: # Update count.
count = count + 1
return total / count # Return average.
That should be easily adaptable to your own requirements. The sum is a nice feature to "cache" information which may make your code even faster. By that I mean: if you need to work out the sum or average, you can work it out only when the data changes, and using the minimal necessary calculations.
The alternative would be a function which added up all ten numbers when requested, something that would be slower than the single subtract/add when loading another value into the buffer.